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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Donut/RVL-CDIP/Fine_tune_Donut_on_toy_RVL_CDIP_(document_image_classification).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set-up environment\n",
        "\n",
        "First, let's install the relevant libraries: 🤗 Transformers & Datasets, and Sentencepiece (the latter is used for tokenization)."
      ],
      "metadata": {
        "id": "DNMqJ821yNVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ot1nP9YHz8co",
        "outputId": "cc0cc2d6-029e-4962-f187-a0f107d12464"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets sentencepiece"
      ],
      "metadata": {
        "id": "OqcGNPJHyOlt"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load dataset\n",
        "\n",
        "Next, let's load a dataset from the hub. I've prepared a toy dataset which is a small subset of [RVL-CDIP](https://paperswithcode.com/dataset/rvl-cdip), consisting of (image, text) pairs. The text is what the model should learn to generate given the visual input.\n",
        "\n",
        "Here, it's important to understand how targets should be prepared for Donut: you need to create a string called \"ground_truth\" (or \"ground_truths\", in case there are multiple possible ground truth target sequences, see DocVQA). This is a string format of a dictionary (JSON dumped), containing either a `gt_parse` or `gt_parses` key. \n",
        "\n",
        "For document image classification, the `gt_parse` follows the format of `{\"class\" : {class_name}}`, for example, `{\"class\" : \"scientific_report\"}` or `{\"class\" : \"presentation\"}`.\n",
        "\n",
        "The notebook for creating a 🤗 Dataset (custom data) for Donut can be found in my Github repo [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Donut/RVL-CDIP/Preparing_an_image_classification_dataset_for_Donut.ipynb).\n",
        "\n",
        "Let's load the dataset first:\n",
        "\n"
      ],
      "metadata": {
        "id": "kWYic8VNyDNU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"nielsr/rvl_cdip_10_examples_per_class_donut\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
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        "id": "5hU27XC2yEot",
        "outputId": "2efd9115-57fc-40ea-fead-7832b96c9765"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af\n",
            "WARNING:datasets.builder:Reusing dataset parquet (/root/.cache/huggingface/datasets/nielsr___parquet/nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {id: label for id, label in enumerate(dataset['train'].features['label'].names)}\n",
        "print(id2label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "St2CS-K4mmfi",
        "outputId": "e1b86453-bf56-4859-cd93-6cd00711c787"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'letter', 1: 'form', 2: 'email', 3: 'handwritten', 4: 'advertisement', 5: 'scientific report', 6: 'scientific publication', 7: 'specification', 8: 'file folder', 9: 'news article', 10: 'budget', 11: 'invoice', 12: 'presentation', 13: 'questionnaire', 14: 'resume', 15: 'memo'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check out an example:"
      ],
      "metadata": {
        "id": "o8QKzs2Fj-PD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset['train'][0]\n",
        "example"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWl6_rbGAykC",
        "outputId": "3325fdd6-2d3b-43a8-b8c3-9ce8abd928d3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ground_truth': '{\"gt_parse\": {\"class\" : \"letter\"}}',\n",
              " 'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=762x1000 at 0x7F2FD79DB9D0>,\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As can be seen, we prepared the \"ground_truth\" key, which is a string-formatted dictionary containing a single `gt_parse` key."
      ],
      "metadata": {
        "id": "UdzKlEsWkAfc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset[\"train\"][0]\n",
        "example[\"ground_truth\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LC4wUbnnNw2-",
        "outputId": "958a0856-ad2c-4e8a-d341-011a0204a6ef"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"gt_parse\": {\"class\" : \"letter\"}}'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One can use Python's `ast.literal_eval` function to turn the string into a Python dictionary. This helps verifying we have created the gt_parse string in the correct way:"
      ],
      "metadata": {
        "id": "9OicQM1dkGXe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval\n",
        "\n",
        "literal_eval(example[\"ground_truth\"])['gt_parse']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cLsTrU6UXq2",
        "outputId": "c829317a-03b3-4167-b036-6fb64607a398"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'class': 'letter'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load model and processor\n",
        "\n",
        "Next, we'll load the model itself (Donut is an instance of the [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) class), as well as its processor (`DonutProcessor`), which can be used to prepare data for the model.\n",
        "\n",
        "We'll update some things; namely the max sequence length of the decoder, as well as the size of the images. Note that this impacts the amount of memory used when fine-tuning the model.\n",
        "\n",
        "Note that the exact hyperparameters can be found here: https://github.com/clovaai/donut/blob/master/config/train_rvlcdip.yaml."
      ],
      "metadata": {
        "id": "BCjMK93Cz3zf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import VisionEncoderDecoderConfig\n",
        "\n",
        "max_length = 8\n",
        "# image_size = [2560, 1920]\n",
        "# let's use a smaller image size (height, width) because otherwise OOM\n",
        "# the higher the resolution, the better the results will be\n",
        "# so if you have a big GPU, feel free to increase\n",
        "image_size = [1280, 960]\n",
        "\n",
        "# update image_size of the encoder\n",
        "# during pre-training, a larger image size was used\n",
        "config = VisionEncoderDecoderConfig.from_pretrained(\"nielsr/donut-base\")\n",
        "config.encoder.image_size = image_size # (height, width)\n",
        "# update max_length of the decoder (for generation)\n",
        "config.decoder.max_length = max_length\n",
        "# TODO we should actually update max_position_embeddings and interpolate the pre-trained ones:\n",
        "# https://github.com/clovaai/donut/blob/0acc65a85d140852b8d9928565f0f6b2d98dc088/donut/model.py#L602"
      ],
      "metadata": {
        "id": "ahkkeo8_o69z"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DonutProcessor, VisionEncoderDecoderModel, BartConfig\n",
        "\n",
        "processor = DonutProcessor.from_pretrained(\"nielsr/donut-base\")\n",
        "model = VisionEncoderDecoderModel.from_pretrained(\"nielsr/donut-base\", config=config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84TkZP5zz4hE",
        "outputId": "28e8c354-1595-4dc4-933d-3dbea8fed10f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we update some settings which differ from pretraining; namely the size of the images + no rotation required\n",
        "# source: https://github.com/clovaai/donut/blob/master/config/train_cord.yaml\n",
        "processor.feature_extractor.size = image_size[::-1] # should be (width, height)\n",
        "processor.feature_extractor.do_align_long_axis = False"
      ],
      "metadata": {
        "id": "RQaAZH7MPAmr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare dataset\n",
        "\n",
        "The first thing we'll do is add the class names as added tokens to the vocabulary of the decoder of Donut, and the corresponding tokenizer.\n",
        "\n",
        "This will result in a slight increase in performance, as otherwise a class might be split up into multiple subword tokens (e.g. the class \"advertisement\" might be split up into \"adv\", \"ertisement\"). It is beneficial to let the model just learn a single embedding vector for the token \"advertisement\"."
      ],
      "metadata": {
        "id": "p5D7ifcgM5Hu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(processor.tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEPtk1N9IdVC",
        "outputId": "c3616976-846c-4ca4-f4fc-419d3ad5f7a6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57525"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List\n",
        "\n",
        "def add_tokens(list_of_tokens: List[str]):\n",
        "    \"\"\"\n",
        "    Add tokens to tokenizer and resize the token embeddings\n",
        "    \"\"\"\n",
        "    newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n",
        "    if newly_added_num > 0:\n",
        "        model.decoder.resize_token_embeddings(len(processor.tokenizer))"
      ],
      "metadata": {
        "id": "CfJMb2o31AA-"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "additional_tokens = [\"<advertisement/>\", \"<budget/>\", \"<email/>\", \"<file_folder/>\", \"<form/>\", \"<handwritten/>\", \"<invoice/>\",\n",
        "  \"<letter/>\", \"<memo/>\", \"<news_article/>\", \"<presentation/>\", \"<questionnaire/>\", \"<resume/>\",\n",
        "  \"<scientific_publication/>\", \"<scientific_report/>\", \"<specification/>\"]\n",
        "\n",
        "add_tokens(additional_tokens)"
      ],
      "metadata": {
        "id": "_dnEFkj71UE1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processor.tokenizer.convert_tokens_to_ids([\"<file_folder/>\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhDuDqIoB0Et",
        "outputId": "fbd71256-3090-40db-ee7a-d3e7dac5dc9e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[57528]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(processor.tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuD68EeJI64_",
        "outputId": "d83a7fa5-e47b-41f7-da3e-cbcf83ae6f03"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57541"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we create a regular PyTorch Dataset. The class below returns `(pixel_values, labels)` pairs for each item of the dataset.\n",
        "\n",
        "It's all we need for training the model!"
      ],
      "metadata": {
        "id": "kIYcX-JA15YY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import random\n",
        "from typing import Any, List, Tuple\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class DonutDataset(Dataset):\n",
        "    \"\"\"\n",
        "    DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)\n",
        "    Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt),\n",
        "    and it will be converted into input_tensor(vectorized image) and input_ids(tokenized string).\n",
        "    Args:\n",
        "        dataset_name_or_path: name of dataset (available at huggingface.co/datasets) or the path containing image files and metadata.jsonl\n",
        "        max_length: the max number of tokens for the target sequences\n",
        "        split: whether to load \"train\", \"validation\" or \"test\" split\n",
        "        ignore_id: ignore_index for torch.nn.CrossEntropyLoss\n",
        "        task_start_token: the special token to be fed to the decoder to conduct the target task\n",
        "        prompt_end_token: the special token at the end of the sequences\n",
        "        sort_json_key: whether or not to sort the JSON keys\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset_name_or_path: str,\n",
        "        max_length: int,\n",
        "        split: str = \"train\",\n",
        "        ignore_id: int = -100,\n",
        "        task_start_token: str = \"<s>\",\n",
        "        prompt_end_token: str = None,\n",
        "        sort_json_key: bool = True,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.max_length = max_length\n",
        "        self.split = split\n",
        "        self.ignore_id = ignore_id\n",
        "        self.task_start_token = task_start_token\n",
        "        self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token\n",
        "        self.sort_json_key = sort_json_key\n",
        "\n",
        "        self.dataset = load_dataset(dataset_name_or_path, split=self.split)\n",
        "        self.dataset_length = len(self.dataset)\n",
        "\n",
        "        self.gt_token_sequences = []\n",
        "        for sample in self.dataset:\n",
        "            ground_truth = json.loads(sample[\"ground_truth\"])\n",
        "            if \"gt_parses\" in ground_truth:  # when multiple ground truths are available, e.g., docvqa\n",
        "                assert isinstance(ground_truth[\"gt_parses\"], list)\n",
        "                gt_jsons = ground_truth[\"gt_parses\"]\n",
        "            else:\n",
        "                assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n",
        "                gt_jsons = [ground_truth[\"gt_parse\"]]\n",
        "\n",
        "            self.gt_token_sequences.append(\n",
        "                [\n",
        "                    self.json2token(\n",
        "                        gt_json,\n",
        "                        update_special_tokens_for_json_key=self.split == \"train\",\n",
        "                        sort_json_key=self.sort_json_key,\n",
        "                    )\n",
        "                    + processor.tokenizer.eos_token\n",
        "                    for gt_json in gt_jsons  # load json from list of json\n",
        "                ]\n",
        "            )\n",
        "\n",
        "        self.add_tokens([self.task_start_token, self.prompt_end_token])\n",
        "        self.prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)\n",
        "\n",
        "    def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n",
        "        \"\"\"\n",
        "        Convert an ordered JSON object into a token sequence\n",
        "        \"\"\"\n",
        "        if type(obj) == dict:\n",
        "            if len(obj) == 1 and \"text_sequence\" in obj:\n",
        "                return obj[\"text_sequence\"]\n",
        "            else:\n",
        "                output = \"\"\n",
        "                if sort_json_key:\n",
        "                    keys = sorted(obj.keys(), reverse=True)\n",
        "                else:\n",
        "                    keys = obj.keys()\n",
        "                for k in keys:\n",
        "                    if update_special_tokens_for_json_key:\n",
        "                        self.add_tokens([fr\"<s_{k}>\", fr\"</s_{k}>\"])\n",
        "                    output += (\n",
        "                        fr\"<s_{k}>\"\n",
        "                        + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n",
        "                        + fr\"</s_{k}>\"\n",
        "                    )\n",
        "                return output\n",
        "        elif type(obj) == list:\n",
        "            return r\"<sep/>\".join(\n",
        "                [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n",
        "            )\n",
        "        else:\n",
        "            obj = str(obj)\n",
        "            if f\"<{obj}/>\" in additional_tokens:\n",
        "                obj = f\"<{obj}/>\"  # for categorical special tokens\n",
        "            return obj\n",
        "    \n",
        "    def add_tokens(self, list_of_tokens: List[str]):\n",
        "        \"\"\"\n",
        "        Add tokens to tokenizer and resize the token embeddings of the decoder\n",
        "        \"\"\"\n",
        "        newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n",
        "        if newly_added_num > 0:\n",
        "            model.decoder.resize_token_embeddings(len(processor.tokenizer))\n",
        "    \n",
        "    def __len__(self) -> int:\n",
        "        return self.dataset_length\n",
        "\n",
        "    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
        "        \"\"\"\n",
        "        Load image from image_path of given dataset_path and convert into input_tensor and labels\n",
        "        Convert gt data into input_ids (tokenized string)\n",
        "        Returns:\n",
        "            input_tensor : preprocessed image\n",
        "            input_ids : tokenized gt_data\n",
        "            labels : masked labels (model doesn't need to predict prompt and pad token)\n",
        "        \"\"\"\n",
        "        sample = self.dataset[idx]\n",
        "\n",
        "        # pixel values (we remove the batch dimension)\n",
        "        pixel_values = processor(sample[\"image\"].convert(\"RGB\"), random_padding=self.split == \"train\", return_tensors=\"pt\").pixel_values\n",
        "        pixel_values = pixel_values.squeeze()\n",
        "\n",
        "        # labels, which are the input ids of the target sequence\n",
        "        target_sequence = random.choice(self.gt_token_sequences[idx])  # can be more than one, e.g., DocVQA Task 1\n",
        "        input_ids = processor.tokenizer(\n",
        "            target_sequence,\n",
        "            add_special_tokens=False,\n",
        "            max_length=self.max_length,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            return_tensors=\"pt\",\n",
        "        )[\"input_ids\"].squeeze(0)\n",
        "\n",
        "        labels = input_ids.clone()\n",
        "        labels[labels == processor.tokenizer.pad_token_id] = self.ignore_id  # model doesn't need to predict pad token\n",
        "        # labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id  # model doesn't need to predict prompt (for VQA)\n",
        "        \n",
        "        encoding = dict(pixel_values=pixel_values,\n",
        "                        labels=labels)\n",
        "        \n",
        "        return encoding"
      ],
      "metadata": {
        "id": "rFrOo4yNeKKN"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create the dataset:"
      ],
      "metadata": {
        "id": "zH-4ldpil_wW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = DonutDataset(\"nielsr/rvl_cdip_10_examples_per_class_donut\", max_length=max_length,\n",
        "                             split=\"train\", task_start_token=\"<s_rvlcdip>\", prompt_end_token=\"<s_rvlcdip>\",\n",
        "                             sort_json_key=False, # rvlcdip dataset is preprocessed, so no need for this\n",
        "                             )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oz-R5pUyTU-N",
        "outputId": "2b1be05f-2ba8-457a-e52e-8e274fe5d146"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af\n",
            "WARNING:datasets.builder:Reusing dataset parquet (/root/.cache/huggingface/datasets/nielsr___parquet/nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the first item of this dataset:"
      ],
      "metadata": {
        "id": "Ksl7GCka2n4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oi9V_Kk0_PTC",
        "outputId": "9df7cad1-dc8c-4839-c183-be5069c57853"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'labels': tensor([57541, 57532, 57542,     2,  -100,  -100,  -100,  -100]),\n",
              " 'pixel_values': tensor([[[-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          ...,\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.]],\n",
              " \n",
              "         [[-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          ...,\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.]],\n",
              " \n",
              "         [[-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          ...,\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.],\n",
              "          [-1., -1., -1.,  ..., -1., -1., -1.]]])}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create PyTorch DataLoader\n",
        "\n",
        "Now that we've created a PyTorch dataset, we can create a corresponding DataLoader:\n",
        "\n"
      ],
      "metadata": {
        "id": "IE3KILONztGa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# I'm using a small batch size to make sure it fits in the memory Colab provides\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True)"
      ],
      "metadata": {
        "id": "re7LWSJJzuLs"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check out the first batch:"
      ],
      "metadata": {
        "id": "iTP3MMpgmMXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch = next(iter(train_dataloader))\n",
        "print(batch.keys())"
      ],
      "metadata": {
        "id": "ZRyUfbGh0k99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72488479-4636-4131-dd3e-995f92fc1762"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['pixel_values', 'labels'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the labels:"
      ],
      "metadata": {
        "id": "Qe2WHSg3mWCe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for id in batch['labels'][0].tolist():\n",
        "  if id != -100:\n",
        "    print(processor.decode([id]))\n",
        "  else:\n",
        "    print(id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SaFVc_U9IuJR",
        "outputId": "3f9a3c3c-e804-41db-d9c2-8bf7960c570b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s_class>\n",
            "<email/>\n",
            "</s_class>\n",
            "</s>\n",
            "-100\n",
            "-100\n",
            "-100\n",
            "-100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's visualize the pixel values by denormalizing:"
      ],
      "metadata": {
        "id": "ojj0sD-v1Jb4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "mean = (0.485, 0.456, 0.406)\n",
        "std = (0.229, 0.224, 0.225)\n",
        "\n",
        "# unnormalize\n",
        "reconstructed_image = (batch['pixel_values'][0] * torch.tensor(std)[:, None, None]) + torch.tensor(mean)[:, None, None]\n",
        "# unrescale\n",
        "reconstructed_image = reconstructed_image * 255\n",
        "# convert to numpy of shape HWC\n",
        "reconstructed_image = torch.moveaxis(reconstructed_image, 0, -1)\n",
        "image = Image.fromarray(reconstructed_image.numpy().astype(np.uint8))\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2vI3drkU1DrP",
        "outputId": "30a9de60-f9ee-4eff-cf7d-0bbaf8c25e9d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=960x1280 at 0x7F2EF9EAB690>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This indeed looks like an email. So data preparation seems ok!"
      ],
      "metadata": {
        "id": "tGUBJOG1m7QK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train model\n",
        "\n",
        "Ok there's one additional thing before we can start training the model: during training, the model can create the `decoder_input_ids` (the decoder inputs) automatically based on the `labels` (by simply shifting them one position to the right, prepending the `decoder_start_token_id` and replacing labels which are -100 by the `pad_token_id`). Therefore, we need to set those variables, to make sure the `decoder_input_ids` are created automatically.\n",
        "\n",
        "This ensures we only need to prepare labels for the model. Theoretically you can also create the `decoder_input_ids` yourself and not set the 2 variables below. This is what the original authors of Donut did."
      ],
      "metadata": {
        "id": "T-AfokzKrxPU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
        "model.config.decoder_start_token_id = processor.tokenizer.convert_tokens_to_ids(['<s_rvlcdip>'])[0]"
      ],
      "metadata": {
        "id": "VgRLEVMdc1lx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sanity check\n",
        "print(\"Pad token ID:\", processor.decode([model.config.pad_token_id]))\n",
        "print(\"Decoder start token ID:\", processor.decode([model.config.decoder_start_token_id]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTn0nmssdRWE",
        "outputId": "bef16842-6025-43b9-82aa-1af22f8e64c5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pad token ID: <pad>\n",
            "Decoder start token ID: <s_rvlcdip>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's train!"
      ],
      "metadata": {
        "id": "bRNf51BKnsbi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu' \n",
        "model.to(device)\n",
        "optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-5)\n",
        "\n",
        "for epoch in range(10):\n",
        "    print(\"Epoch:\", epoch+1)\n",
        "    model.train()\n",
        "    for i, batch in enumerate(tqdm(train_dataloader)):\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        pixel_values = batch[\"pixel_values\"]\n",
        "        labels = batch[\"labels\"]\n",
        "\n",
        "        outputs = model(pixel_values=pixel_values,\n",
        "                        labels=labels)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        if i % 100 == 0:\n",
        "            print(\"Loss:\", loss.item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859,
          "referenced_widgets": [
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        "outputId": "ceb4728f-0014-40cc-8d04-303357ef0bb9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1\n"
          ]
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          "text": [
            "Loss: 21.841978073120117\n",
            "Loss: 2.5098507404327393\n",
            "Epoch: 2\n"
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          "text": [
            "Loss: 1.157701015472412\n",
            "Loss: 0.784305214881897\n",
            "Epoch: 3\n"
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          "text": [
            "Loss: 0.581136167049408\n",
            "Loss: 0.49410971999168396\n",
            "Epoch: 4\n"
          ]
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          "text": [
            "Loss: 0.2765415906906128\n",
            "Loss: 0.5049015879631042\n",
            "Epoch: 5\n"
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          "text": [
            "Loss: 0.18805643916130066\n",
            "Loss: 0.6364682912826538\n",
            "Epoch: 6\n"
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            "Loss: 0.488409161567688\n",
            "Loss: 0.6658070683479309\n",
            "Epoch: 7\n"
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            "Loss: 0.09549322724342346\n",
            "Loss: 0.20991729199886322\n",
            "Epoch: 8\n"
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          "text": [
            "Loss: 0.5973004102706909\n",
            "Loss: 0.745875895023346\n",
            "Epoch: 9\n"
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          "text": [
            "Loss: 0.005445633549243212\n",
            "Loss: 0.3268482983112335\n",
            "Epoch: 10\n"
          ]
        },
        {
          "output_type": "display_data",
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          "name": "stdout",
          "text": [
            "Loss: 0.22070863842964172\n",
            "Loss: 0.15976710617542267\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate\n",
        "\n",
        "Finally, we compute the accuracy on the test set.\n",
        "\n",
        "Note that this will be far from perfect as we were just training on a toy dataset. The entire dataset consists of 400,000 images.\n",
        "\n",
        "We'll verify whether the model is better than random (which means 1/number of classes = 1/16 = 0.0625) accuracy.\n",
        "\n",
        "We'll use the token2json method of the processor to turn the generated sequences into JSON."
      ],
      "metadata": {
        "id": "249vWlG6Sxao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "model.eval()\n",
        "\n",
        "dataset = load_dataset(\"nielsr/rvl_cdip_10_examples_per_class_donut\", split=\"test\")\n",
        "\n",
        "output_list = []\n",
        "accs = []\n",
        "for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):\n",
        "    # prepare encoder inputs\n",
        "    pixel_values = processor(sample[\"image\"].convert(\"RGB\"), return_tensors=\"pt\").pixel_values\n",
        "    pixel_values = pixel_values.to(device)\n",
        "    # prepare decoder inputs\n",
        "    task_prompt = \"<s_rvlcdip>\"\n",
        "    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    decoder_input_ids = decoder_input_ids.to(device)\n",
        "    \n",
        "    # autoregressively generate sequence\n",
        "    outputs = model.generate(\n",
        "            pixel_values,\n",
        "            decoder_input_ids=decoder_input_ids,\n",
        "            max_length=model.decoder.config.max_position_embeddings,\n",
        "            early_stopping=True,\n",
        "            pad_token_id=processor.tokenizer.pad_token_id,\n",
        "            eos_token_id=processor.tokenizer.eos_token_id,\n",
        "            use_cache=True,\n",
        "            num_beams=1,\n",
        "            bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "            return_dict_in_generate=True,\n",
        "        )\n",
        "\n",
        "    # turn into JSON\n",
        "    seq = processor.batch_decode(outputs.sequences)[0]\n",
        "    seq = seq.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip()  # remove first task start token\n",
        "    seq = processor.token2json(seq)\n",
        "\n",
        "    ground_truth = json.loads(sample[\"ground_truth\"])\n",
        "    gt = ground_truth[\"gt_parse\"]\n",
        "    score = float(seq[\"class\"] == gt[\"class\"])\n",
        "\n",
        "    accs.append(score)\n",
        "\n",
        "    output_list.append(seq)\n",
        "\n",
        "scores = {\"accuracies\": accs, \"mean_accuracy\": np.mean(accs)}\n",
        "print(scores, f\"length : {len(accs)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "c5d5b69977b44dada9c97013007a5544",
            "e3546423ea5e4e0b896354f1c4bf2c31",
            "a0594e1044664f0caf2fc9db134593c8",
            "50bdbfb36aad45b19ea619f14f5d4374",
            "34f9edd36ee240e7ad52035ed455fd58",
            "89c65ce3abdf477b9074af9bf7dcf90d",
            "f3d330b532704e15b75834021091bc06",
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            "fdc076a5e7684ea7bf49cbc06cde34fb",
            "9bef0c75563c4dd5ad1c1a8aa9b11848",
            "e39493e44c8a424ab79c69a0943ff885"
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        },
        "id": "GdqNUaYzS_9D",
        "outputId": "0a144f9f-3597-45de-b483-0ebeeb6d693d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af\n",
            "WARNING:datasets.builder:Reusing dataset parquet (/root/.cache/huggingface/datasets/nielsr___parquet/nielsr--rvl_cdip_10_examples_per_class_donut-f7a67080e6d136af/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/160 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracies': [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], 'mean_accuracy': 0.45625} length : 160\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores[\"mean_accuracy\"]"
      ],
      "metadata": {
        "id": "GqQBW27Oiohq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "That looks a lot better than random accuracy, suggesting that our model seems to learn well!"
      ],
      "metadata": {
        "id": "QhcwrGVosaOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "386z_wjhjpiN"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}